Our aim is to update the physicians working with COVID-19 patients about this potential complication and hope that understanding of these proposed mechanisms can provide an opportunity for the physicians for early diagnosis or even better, help prevent this complication.Eutectic gallium indium (EGaIn), a Ga-based liquid metal alloy holds great promise for designing next generation core-shell nanoparticles (CSNs). A shearing assisted ligand-stabilization method has shown promise as a synthetic method for these CSNs; however, determining the role of the ligand on stabilization demands an understanding of the surface chemistry of the ligand-nanoparticle interface. EGaIn CSNs have been created functionalized with aliphatic carboxylates of different chain length allowing a fundamental investigation on ligand stabilization of EGaIn CSNs. Raman and diffuse reflectance Fourier transform spectroscopies (DRIFTS) confirm reaction of the ligand with the oxide shell of the EGaIn nanoparticles. Changing the length of the alkyl chain in the aliphatic carboxylates (C2-C18) may influence the size and structural stability of EGaIn CSNs, which is easily monitored using atomic force microscopy (AFM). No matter how large the carboxylate ligand, there is no obvious effect on the size of the EGaIn CSNs, except the particle size got more uniform when coated with longer chain carboxylates. The AFM force distance (F-D) measurements are used to measure the stiffness of the carboxylate coated EGaIn CSN. In corroboration with DRIFTS analysis, the stiffness studies show that the alkyl chains undergo conformational changes upon compression.Information dissemination has changed rapidly in recent years with the emergence of social media which provides online platforms for people worldwide to share their thoughts, activities, emotions, and build social relationships. Hence, modeling information diffusion has become an important area of research in the field of network analysis. It involves the mathematical modeling of the movement of information and study the information spread pattern. In this paper, we attempt to model information propagation in online social networks using a nature-inspired approach based on a modified forest-fire model. https://www.selleckchem.com/products/vardenafil.html A slight spark can start a wildfire in a forest, and the spread of this fire depends on vegetation, weather, and topography, which may act as fuel. On similar lines, we labeled users who haven't joined the network yet as Empty, existing users as Tree, and information as Fire. The spread of information across online social networks depends upon users-followers relationships, the significance of the topic, and other such features. We introduce a novel Burnt state to the traditional forest-fire model to represent non-spreaders in the network. We validate our method on six real-world data-sets extracted from Twitter and conclude that the proposed model performs reasonably well in predicting information diffusion.In-situ thermal cycling neutron diffraction experiments were employed to unravel the effect of thermal history on the evolution of phase stability and internal stresses during the additive manufacturing (AM) process. While the fully-reversible martensite-austenite phase transformation was observed in the earlier thermal cycles where heating temperatures were higher than Af, the subsequent damped thermal cycles exhibited irreversible phase transformation forming reverted austenite. With increasing number of thermal cycles, the thermal stability of the retained austenite increased, which decreased the coefficient of thermal expansion. However, martensite revealed higher compressive residual stresses and lower dislocation density, indicating inhomogeneous distributions of the residual stresses and microstructures on the inside and on the surface of the AM component. The compressive residual stresses that acted on the martensite resulted preferentially from transformation strain and additionally from thermal misfit strain, and the decrease in the dislocation density might have been due to the strong recovery effect near the Ac1 temperature.Scholars and practitioners have recognized the importance of supply chain (SC) resilience. However, it remains unclear how to build SC resilience and whether SC resilience can enhance firm performance and bring values to customers. By analyzing data collected from 206 manufacturers in China, this study empirically examines how firms implement different information technology (IT) patterns (exploitative versus explorative) with SC partners to achieve supplier and customer resilience from information processing theory, and examines the performance implications of these two dimensions of SC resilience. In addition, this study also investigates how IT ambidexterity reconciles the paradox between IT exploitation and IT exploration in enhancing SC resilience. The results show that both supplier and customer resilience could improve SC performance. To achieve the two aspects of SC resilience, only explorative use of IT with suppliers and customers have significant effects. The results also show that the ambidextrous use of IT on the customer side takes effect. The exploitative and explorative use of IT complement each other to improve customer resilience. The findings of this study contribute to IT and SC resilience literature.We study progression-free sets in the abelian groups G = ( Z m n , + ) . Let r k ( Z m n ) denote the maximal size of a set S ⊂ Z m n that does not contain a proper arithmetic progression of length k. We give lower bound constructions, which e.g. include that r 3 ( Z m n ) ? C m ( ( m + 2 ) / 2 ) n n , when m is even. When m = 4 this is of order at least 3 n / n ≫  G  0.7924 . Moreover, if the progression-free set S ⊂ Z 4 n satisfies a technical condition, which dominates the problem at least in low dimension, then  S  ? 3 n holds. We present a number of new methods which cover lower bounds for several infinite families of parameters m, k, n, which includes for example r 6 ( Z 125 n ) ? ( 85 - o ( 1 ) ) n . For r 3 ( Z 4 n ) we determine the exact values, when n ? 5 , e.g. r 3 ( Z 4 5 ) = 124 , and for r 4 ( Z 4 n ) we determine the exact values, when n ? 4 , e.g. r 4 ( Z 4 4 ) = 128 . With regard to affine caps, i.e. sets without 3 points on a line, the new methods asymptotically improve the known lower bounds, when m = 4 and m = 5 in Z 4 n from 2 .